All in the family: partisan disagreement and electoral mobilization in intimate networks—a spillover experiment

We advance the debate about the impact of political disagreement in social networks on electoral participation by addressing issues of causal inference common in network studies, focusing on voters' most important context of interpersonal influence: the household. We leverage a randomly assigned spillover experiment conducted in the United Kingdom, combined with a detailed database of pretreatment party preferences and public turnout records, to identify social influence within heterogeneous and homogeneous partisan households. Our results show that intrahousehold mobilization effects are larger as a result of campaign contact in heterogeneous than in homogeneous partisan households, and larger still when the partisan intensity of the message is exogenously increased, suggesting discussion rather than behavioral contagion as a mechanism. Our results qualify findings from influential observational studies and suggest that within intimate social networks, negative correlations between political heterogeneity and electoral participation are unlikely to result from political disagreement

One Year’s Results from a Server-Based System for Performing Reject Analysis and Exposure Analysis in Computed Radiography

Rejected images represent both unnecessary radiation exposure to patients and inefficiency in the imaging operation. Rejected images are inherent to projection radiography, where patient positioning and alignment are integral components of image quality. Patient motion and artifacts unique to digital image receptor technology can result in rejected images also. We present a centralized, server-based solution for the collection, archival, and distribution of rejected image and exposure indicator data that automates the data collection process. Reject analysis program (RAP) and exposure indicator data were collected and analyzed during a 1-year period. RAP data were sorted both by reason for repetition and body part examined. Data were also stratified by clinical area for further investigation. The monthly composite reject rate for our institution fluctuated between 8% and 10%. Positioning errors were the main cause of repeated images (77.3%). Stratification of data by clinical area revealed that areas where computed radiography (CR) is seldom used suffer from higher reject rates than areas where it is used frequently. S values were log-normally distributed for examinations performed under either manual or automatic exposure control. The distributions were positively skewed and leptokurtic. S value decreases due to radiologic technology student rotations, and CR plate reader calibrations were observed. Our data demonstrate that reject analysis is still necessary and useful in the era of digital imaging. It is vital though that analysis be combined with exposure indicator analysis, as digital radiography is not self-policing in terms of exposure. When combined, the two programs are a powerful tool for quality assurance